B.Parameshwari [3], a new communication paradigm in cooperative

 

                                                                                                                                      

 

B.Parameshwari

Assistant Professor

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Department of

Electronics and

Communication
Engineering,

CJITS,
Yeshwanthapur,T.S

E-mail:[email protected]

  

 

 

ABSTRACT

 

This paper presents, a spectrum
sharing strategy in cooperative cognitive radio network (CCRN). A multi-phase
cooperation architecture is explained and studied with cooperation partner
selection and spectrum sharing among secondary users (SUs).The data of primary
users (PUs) forwarded to the cooperation partners who are selected from SUs,
and then acquire the spectrum access opportunities for their own transmissions
as a reward. The partner selection is modeled as an optimally weighted
bipartite matching problem to maximize the total utility where energy
efficiency is also considered just to increase the utility for the PU-SU
cooperation pairs. By the partner SU further improvisation in the spectrum utilization
is done by sharing the acquired spectrum with the surrounding SUs via
cooperative network coding. At the end the simulation results provided, which
shows that to the dynamic traffic loads in CCRN, the proposed partner selection
and spectrum sharing approach adapts well.

 

Keywords

CCRN,  QoS, 
IUs, MMSE

 

1.    INTRODUCTION

 

The scarcity of spectral resources
has become a severe problem due to the significant growth in commercial
wireless services, in recent years, with the emergence of cooperative communications
in wireless networks 3, a new communication paradigm in cooperative cognitive
radio networks is proposed 4–6, termed cooperative cognitive radio networks
(CCRN). The traditional fixed spectrum allocation is proved inefficient, since
the frequency band is largely under-utilized 1. Cognitive Radio (CR) 2 has
been considered as a promising technology for improve spectrum utilization by
allowing secondary users (SUs) to access spectrum holes unoccupied by primary
users (Pus).

 

The rapid growth in wireless
communications has contributed a huge demand on the deployment of new wireless
services in both the licensed and unlicensed frequency spectrum. However,
recent Studies show the fixed spectrum assignment policy enforced today results
in poor spectrum utilization. To address this problem, cognitive radio 1,2
has emerged as a promising technology to enable the access of the intermittent
periods of unoccupied frequency bands, known as white space or spectrum holes,
and thereby increase the spectral efficiency.

 

The fundamental task of each
Cognitive radio user in cognitive radio networks, in themost primitive sense,
for detection of licensed users, also called as primary users (PUs),

 

 

M.Srujana

Assistant
Professor

Department
of

Electronics
and

Communication
Engineering,

CJITS,
Yeshwanthapur,T.S

E-mail:[email protected]

 

 

 

 

if they are present
and identify the available spectrum if they are absent. This is usually
achieved by sensing the RF environment, process called spectrum sensing 1–4.
The objectives of spectrum sensing are twofold: first, CR users should not
cause harmful interference to PUs by either switching to an available band or
limiting its interference with PUs at an acceptable level and, second, CR users
should efficiently identify the spectrum holes for required throughput and
quality-of service(QoS). Thus, the detection performance in spectrum sensing is
very much crucial to the performance of both primary and CR networks.

 

In the conventional
CCRN formulation, some type of resource allocation problem was addressed, such
as subchannel assignment for SUs, relay assignment, and power control 4– 6.
In 4, the subcarrier assignment, relay assignment, and SU relay strategy
optimization problems were approached with flexible channel cooperation in a
multi-channel CCRN, where a unified optimization framework based on Nash
Bargaining Solutions was developed. In 5, 6, the spectrum leasing problem was
formulated for one PU and multiple SUs as a Stackelberg game and the Nash
equilibrium was derived. A single channel was assumed available, and different
transmissions were divided in time. The consideration of one channel and one PU
in 5, 6 presents a simplification for practical scenarios where there are
typically multiple channels and multiple PUs that coexist in the coverage area
of a base station in the cellular network.

 

A multiphase
cooperation scheme is proposed in order to improve the network utility as well
as the spectrum access opportunity. We assign the selected relaying SUs as the
group of intermediate users (IUs), which cooperate with PUs in traffic relay
and share the spectrum access opportunities with the remaining SUs,
respectively. With the help of IUs, the PUs can improve their own performance
as well as not be involved in such a complicated cooperation scheme with
multiple SUs. Meanwhile, the SUs starving for the spectrum access opportunities
attain what they want as well.

 

Second, an IU
selection scheme is implemented by the maximum weighted bipartite matching
algorithm, and the utility of the cooperating pairs is enhanced by exploiting
the ratio of cooperation pairs’ utility to the total energy consumption with
the consideration of the IUs’ energy efficiency. Third, through the cooperation
among the IUs and the surrounding SUs by using cooperative network coding, the
starving SUs who form a cluster can obtain the transmission opportunities
without consuming too much energy to relay the PUs’ traffic. Conversely, the
IUs’ utility and communication reliability can be enhanced.

 

 

 

4

 

 

 

2.   SYSTEM MODEL

 

As demonstrated in Fig. , we
consider PUs and SUs are uniformly distributed in a CCRN. The data has been
transmitted to the BS over its own licensed channel by a base station (BS)
serves PUs and each PU, given that the spectrums of PUs are orthogonal in
frequency and/or space. access points (APs) coexist in the same area serving
SUs and each SU communicates with its corresponding AP.

 

The first phase cooperation
is between the PU and the selected cluster head IU, while the second phase
cooperation is between the cluster head and other SUs in the cluster. As shown
in Fig. 2, the cooperation between SUs and Pus takes place in a two-phase
cooperation scheme in each time slot . The partner IU selection scheme is first
performed, and then the cluster head IU cooperates with the PU in a TDMA manner
that the PU transmits its package to the cooperating IU and the IU relays PU’s
last package to the BS simultaneously. After the cooperation between PU and IU,
the IU finds the cooperative SUs who form a cluster from the surrounding
starving SUs. Then, the IU and the SUs in the cluster cooperate by cooperative
network coding.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fig. 1.
Scenario of CCRN

 

The channel
conditions are assumed to be stable during a fix time slot , but vary
independently from one slot to another. The spectrum sharing strategy operates
in a time-slotted manner and transmission channels are assumed to conform to a
Rayleigh flat fading model. The CSI is available, which is estimated by
exploiting techniques such as least squares (LS) estimation and minimum
mean-square-error (MMSE)

 

estimation

9.

 

 

 

 

 

 

Fig.
2. Time frame structure for the spectrum sharing strategy

 

The SUs, who participate in the
cooperation with the PUs, send feedbacks with their transmit power values they
want to devote in delivering PUs’ traffic to the BS. In order to improve the
performance of primary network, the BS broadcasts the cooperation selection
requirement to its surrounding SUs. If one SU can serve as the relay for
multiple PUs, it sends different transmit power values corresponding to each PU
to the BS. However, in real networks, some SUs might not be willing to
cooperate with the PU, as it is quite energy consuming to relay PU’s traffic
while the utility gain might be relatively low, i.e., the ratio of utility to
power consumption is low.

 

But the SUs still desire to gain the
secondary transmission opportunities so as to improve the utility. In order to
solve the aforementioned problem, the selected IU cooperates with the remaining
SUs to benefit them. Meanwhile, through the cooperation between cluster head IU
and other SUs in the cluster, the IU can improve its own performance as well.

 

As shown in Fig. 2, The time frame
structure includes two cooperations: the first phase cooperation and the second
phase cooperation. In the IU selection period of the first phase cooperation,
after BS acquires the acknowledgement and the information from potential IUs,
the BS exploits the maximum weighted bipartite matching algorithm to find the
most appropriate cooperative SUs, i.e., the IUs. After partner IU selection,
the PU cooperates with the IU in a TDMA manner. Then, the IU broadcasts its
cooperation requirement to begin the second phase cooperation.

 

The SUs send the
acknowledgement that they want to join into the cooperation with the IU. After
that, the IU transmits its packet towards the associated AP. During this
transmission

 

 

process, the
surrounding SUs (form a cluster) who are involved in the cooperation can
overhear the data. Then, by using network coding, the SUs in a cluster create
new combinations of packets from the received packets and transmit those
towards the respective AP. The cooperation scheme among cluster head IU and SUs
in the cluster is referred as cooperative network coding, in which the IU is
the source and the corresponding AP is the destination, and the SUs form a
cluster to help IU relay the data from the source to the destination.

 

Energy efficiency is
considered in the system by using a ratio of utility to energy, which enables a
tradeoff between utility and energy consumption. IU selection is performed to
select the IUs who cooperate with the PUs. The IUs are a group of SUs that have
better channel conditions than other SUs to relay PUs’ traffic.

 

3.    NUMERICAL
RESULTS

 

In this section, in
comparison with the random selection scheme, the IU selection scheme is
evaluated in a CCRN simulator. The operation factors, e.g., cooperation time

 

allocation and SUs’ power consumption, are also
investigated.

 

As shown in Fig. 1,
there are 4 PUs and 6 SUs in the CCRN. The powers of PUs and SUs vary from 1mW
to 2mW and from 0.5mW to 1.5mW, respectively. The proposed IU selection (IS)
scheme and random selection (RS) scheme, are compared i.e. the performance
obtained by using two different schemes.

 

 

5

 

Network
Utility

 

 

7

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

RS

 

 

 

 

 

 

 

 

 

 

 

IS

 

 

6.5

 

 

 

 

 

 

 

 

 

 

Utality/Power(bits/mW)

6

 

 

 

 

 

 

 

 

 

 

5.5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5

 

 

 

 

 

 

 

 

 

 

 

4.5

4

6

8

10

12

14

16

18

20

 

 

2

 

 

 

 

 

 

Case Index

 

 

 

 

 

 

Fig. 3.
Comparison of the network utility attained by two different schemes

 

In Fig. 4 for the BS under different values of IU’s power is
evaluated, by the impact of choosing the value of . From the candidate SUs in
the cooperation Once BS collects the information; BS chooses an appropriate value
of and to select the IUs, performs the maximum weighted matching. The whole
utility of cooperation pairs is simulated and the utility for different values
of is demonstrated in the figure.

 

 

1.8

 

 

 

 

 

 

 

 

 

 

 

 

 

1.6

 

 

 

 

 

 

 

 

 

 

 

 

 

1.4

 

 

 

 

 

 

 

 

 

 

 

 

Utality/Power(bits/mW)

1.2

 

 

 

 

 

 

 

 

 

 

 

 

1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

alpha=1/5

 

 

 

 

 

0.8

 

 

 

 

 

alpha=1/3

 

 

 

 

 

 

 

 

 

 

alpha=1/2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

alpha=3/5

 

 

 

 

 

 

0.6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

alpha=4/5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

alpha=4.5/5

 

 

 

 

 

 

0.4

 

 

 

 

 

 

 

 

 

 

 

 

 

0.2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

 

 

 

 

 

 

 

IU power
mW

 

 

 

 

 

 

Fig.4. Achieved utility vs. IU’s
power for different values of  .

 

4.    CONCLUSION

 

In this paper, we
have studied and implemented a novel cooperative spectrum sharing approach for
a wireless network consisting of multiple primary and secondary users. we have
seen a spectrum sharing strategy based on two-phase cooperation including an IU
selection scheme in CCRN. The cooperation pairs between PUs and IUs have been
obtained,

 

 

By solving the maximum weighted
bipartite matching problem. Thus we have got the maximum total utility.

 

Further, the energy efficiency have
been considered in the IU selection problem and The selected IU cooperates with
the PU as well as its surrounding SUs. With the help from the IUs the system
utility and the spectrum access opportunity have been improved. With the help
of simulated result we have find that the utility obtained by performing the
proposed partner IU selection scheme is always higher than that attained by the
random selection scheme in our CCRN. In future work, we will analyze the
cooperation between the IU and the surrounding SUs in detail.

 

5.    REFERENCES

1    
Akyildiz, W. Y. Lee,
M. Vuran, and S. Mohanty, “Next

 

Generation/
Dynamic Spectrum Access/Cognitive Radio

 

Wireless Networks: A survey,” ACM
Journal on Computer Networks, vol. 50, no. 13, pp. 2127- 2159, Sep. 2006.

 

2     A.  Goldsmith, 
S.  Jafer,  I. 
Maric,  and  S. 
Srinivasa,

 

“Breaking spectrum gridlock with
cognitive radios: An information theoretic perspective,” in Proceedings of

IEEE, vol.
97, no. 5, pp. 894-914, May 2009.

 

3     O.
Simeone, I. Stanojev, S. Savazzi, Y. Bar-Ness, U.

 

Spagnolini, and R. Pickholtz,
“Spectrum leasing to cooperating secondary ad hoc networks,” IEEE Journal on
Selected Areas in Communications, vol. 26, no. 1, pp. 203-213, Jan. 2008.

 

4    
Y. Han, A.
Pandharipande, and S. Ting, “Cooperative decode-and-forward relaying for
secondary spectrum

access,”IEEETransactionsonWireless

 

Communications,
vol. 8, no. 10, pp. 4945-4950, Oct. 2009.

 

5    
N. Zhang, N. Lu, R.
Lu, J. W. Mark, and X. Shen, “Energy-efficient and trust-aware cooperation in
cognitive radio networks,” in Proceedings of IEEE

 

International
Conference on Communications, pp. 1763-1767, Jun. 2012.

 

 

 

6

 

 

 

6    
T. Elkourdi and O.
Simeone, “Spectrum Leasing via Cooperation with Multilpe Primary Users,” IEEE
Transactions on Vehicular Technology, vol. 61, no. 2, pp. 820-825, Feb. 2012.

 

7     V.
Mahinthan, L. Cai, J. W. Mark and, X. Shen, “Partner

 

Selection Based on
Optimal Power Allocation in Cooperative-Diversity Systems,” IEEE Transactions
on Vehicular Technology, vol. 57, no. 1, pp. 511-520, Jan. 2008.

 

8    
M. Seyfi, S.
Muhaidat, and J. Liang, “Relay Selection in Underlay Cognitive Radio Networks,”
in Proceedings of

 

IEEE
Wireless Communications and Networking Conference, pp. 283-288, Apr. 2012.

 

 

 

 

9    
M. Biguesh and A. B.
Gershman, “Training-Based MIMO Channel Estimation: A Study of Estimator

 

Tradeoffs   and   Optimal  
Training   Signals,”   IEEE

 

Transactions
on Signal Processing, vol. 54, no. 3, pp. 328-339,Mar. 2006.

 

10  “Spectrum
Sharing Strategy using Bipartite Matching for Cooperative Cognitive Radio
Networks”Yujie Tang, Yongkang Liu, Jon W. Mark and Xuemin (Sherman) Shen Centre
for Wireless Communications, University of Waterloo, ON, Canada, N2L 3G1
Globecom 2013 – Cognitive Radio and Networks Symposium

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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